IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v67y1999i2p99-122.html
   My bibliography  Save this article

Using Statistics and Statistical Thinking to Improve Organisational Performance

Author

Listed:
  • S. B. Dransfield
  • N. I. Fisher
  • N. J. Vogel

Abstract

A systematic approach to measuring organisational performance is fundamental to the pursuit of business excellence. As such, the area of organisational performance measurement, and its use of data and analysis to inform business decisions, affords statisticians a potentially high value‐adding opportunity. To be effective in this area, statisticians need to appreciate the differing requirements for statistical information in various management zones of an enterprise. This paper describes a strategy that seeks to link measurement to all facets of organisational performance, particularly to desired business outcomes, and also to mesh measurement with process improvement in a natural way. The use of statistics and statistical thinking is then discussed in this context, with particular focus on the opportunity for statisticians to have a key role at the top decision‐making level of the organisation. We argue that the role requires skills in both advanced technical statistical modelling and analysis, and in statistical thinking. It also requires a preparedness to form an appreciation of the business and management imperatives faced by the leaders of an enterprise, and a willingness to work from this basis. Une approche systématique pour la mesure de performance des entreprises est fondamentale à la poursuite de l'excellence au sein des activités commeciales de l'entreprise. De ce fait, le domaine des mesures des performances de l'entreprise ainsi que son utilisation des études et données pour informer les décisions commerciales, donne aux statisticiens les moyens d'apporter une valeur ajoutée potentiellement élevée. Les statisticiens, pour etre efficasses dans ce domaine, ont besoin d'apprécier et comprendre, les différents besoins de i'information statistique, et ce dans diverses zones d'activité commerciale de l'entreprise. Cet article dtcrit une strategie qui requiert la liaison des mesum a toutes les facettes des performances de l'entreprise, en particulier aux resultats escornptks des activitks commerciales. Cette strategie relie d'une manitre naturelle les mesures au processus d'amtlioration de l'entreprise. L'utilisation des statistiques et de la pensreflection statistique est present dans ce contexte avec une attention particulitre don B l'occasion pour les statisticiens de jouer un rle primordial au niveau le plus Clevc de management de l'entreprise. A notre avis. ce rle requiert des competances en techniques avanh pour la modtlisation et l'analyse statistique, ainsi qu'en reflection statistique. Ce rle requiert Cgalement une aptitute a former une appkciation de l'activitt commerciale de l'entreprise, ainsi que des imphtifs de management des dirigeants de cette dernitre; et la bonne volonte de tnvailler a partir de cette base.

Suggested Citation

  • S. B. Dransfield & N. I. Fisher & N. J. Vogel, 1999. "Using Statistics and Statistical Thinking to Improve Organisational Performance," International Statistical Review, International Statistical Institute, vol. 67(2), pages 99-122, August.
  • Handle: RePEc:bla:istatr:v:67:y:1999:i:2:p:99-122
    DOI: 10.1111/j.1751-5823.1999.tb00417.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1751-5823.1999.tb00417.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1751-5823.1999.tb00417.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Steen Nielsen, 2020. "Management accounting and the idea of machine learning," Economics Working Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
    2. N. I. Fisher & V. N. Nair, 2009. "Quality management and quality practice: Perspectives on their history and their future," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(1), pages 1-28, January.
    3. Liu, Lon-Mu & Bhattacharyya, Siddhartha & Sclove, Stanley L. & Chen, Rong & Lattyak, William J., 2001. "Data mining on time series: an illustration using fast-food restaurant franchise data," Computational Statistics & Data Analysis, Elsevier, vol. 37(4), pages 455-476, October.
    4. Berislav Zmuk, 2015. "Business Sample Survey Measurement on Statistical Thinking and Methods Adoption: The Case of Croatian Small Enterprises," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 13(1), pages 154-166.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:istatr:v:67:y:1999:i:2:p:99-122. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/isiiinl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.